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相关概念视频

Deconvolution01:20

Deconvolution

141
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
141
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

179
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
179
Upsampling01:22

Upsampling

215
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
215
Design Example01:23

Design Example

321
The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
321
Downsampling01:20

Downsampling

137
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
137
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

237
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
237

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相关实验视频

Updated: Jun 13, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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深度学习作为数字信号处理设计的高效工具.

Andrey Pryamikov1

  • 1Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow, Russia. pryamikov@mail.ru.

Light, science & applications
|September 10, 2024
PubMed
概括

在人工神经网络中广泛使用的反向传播算法为光学系统提供了高效的数字信号处理. 这种深度学习方法可以实现具有成本效益的,低复杂度的信号处理设计.

科学领域:

  • 人工智能的人工智能
  • 光学通信是指光学通信.
  • 信号处理 信号处理

背景情况:

  • 反向传播算法是人工神经网络训练的基石.
  • 光纤传输系统需要高效的数字信号处理 (DSP) 方案.
  • 目前的DSP设计可能面临复杂性和成本效益方面的挑战.

研究的目的:

  • 在光纤传输系统中探索反向传播算法的应用.
  • 调查用于DSP设计的深度学习框架的潜力.
  • 为具有成本效益和低复杂度的DSP展示一个新的范式.

主要方法:

  • 应用反向传播算法来开发DSP方案.
  • 在DSP框架内利用深度学习原则.
  • 评估拟议的DSP设计的效率和复杂性.

主要成果:

  • 反向传播算法证明了在光纤系统中的DSP的有效性.
  • 深度学习框架为DSP设计提供了一种新的方法.
  • 开发的范式实现了高效率,低复杂性和成本.

结论:

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  • 基于反向传播的深度学习为光学DSP提供了一个强大的工具.
  • 这种方法有助于创建先进,高效和经济的光学传输系统.
  • 人工智能融入DSP标志着光通信技术的重大进步.